Predictor-based neural dynamic surface control for distributed formation tracking of multiple marine surface vehicles with improved transient performance

In this paper, we investigate the distributed formation tracking problem of multiple marine surface vehicles with model uncertainty and time-varying ocean disturbances induced by wind, waves, and ocean currents. The objective is to achieve a collective tracking with a time-varying trajectory, which can only be accessed by a fraction of follower vehicles. A novel predictor-based neural dynamic surface control design approach is proposed to develop the distributed adaptive formation controllers. We use prediction errors, rather than tracking errors, to construct the neural adaptive laws, which enable the fast identification of the vehicle dynamics without incurring high-frequency oscillations in control signals. We establish the stability properties of the closed-loop network via Lyapunov analysis, and quantify the transient performance by deriving the truncated L2 norms of the derivatives of neural weights, which we demonstrate to be smaller than the classical neural dynamic surface control design approach. We also extend the above result to the distributed formation tracking using the relative position information of vehicles, and the advantage is that the velocity information of neighbors and leader are required. Finally, we give the comparative studies to illustrate the performance improvement of the proposed method.摘要创新点(1)本文提出了一种新型基于预估器的神经网络动态面控制器设计结构, 该结构能够显著地提高系统的暂态控制性能。(2)本文针对含模型参数不确定与海洋环境扰动下的海洋航行器, 提出了两种分布式队形跟踪控制算法, 所提控制算法仅利用邻居信息进行反馈。(3)与传统神经网络动态面控制方法采用跟踪误差学习不同, 本文提出采用预估误差进行神经网络权值在线更新, 能够实现对系统不确定性的快速自适应并且避免控制信号振荡。(4)本文严格证明了所提基于预估器的神经网络动态面控制结构优于传统神经网络动态面控制方法, 即所提控制器结构中神经网络权值导数的截断L2范式更小。